Optimizing runtime environments

ABSTRACT

A system and method for optimizing runtime environments for applications by running the applications in a plurality of runtime environments and iteratively selecting and creating new runtime environments based on a fitness score determined for the plurality of runtime environments.

TECHNICAL FIELD

The present disclosure generally relates to optimizing runtimeenvironments for applications.

BACKGROUND

Developers develop software applications in a development environment.The development environment is usually much more robust than necessaryfor running the software applications. Having a very robust developmentenvironment allows for developers to test and develop softwareapplications without the software applications running into resourcelimitation issues. However, when deploying software applications on theeventual runtime environment, it would be inefficient and a waste toassign additional resources that is rarely used and/or unneeded by thesoftware application. However, it is very difficult to determine anoptimally efficient runtime environment. Different settings anddifferent situations may change the amount of resources necessary. Assuch, runtime environments tend to be inefficient and overpowered.Applicant recognizes that a system for determining an optimized runtimeenvironment would be beneficial.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a block diagram of an example computing system that is adaptedfor optimizing runtime environments.

FIG. 2 is a block diagram of an example computer system suitable forimplementing one or more devices of the computing system in FIG. 1.

FIG. 3 is a flow diagram illustrating an example process for optimizingruntime environments.

Embodiments of the present disclosure and their advantages are bestunderstood by referring to the detailed description that follows. Itshould be appreciated that like reference numerals are used to identifylike elements illustrated in one or more of the figures, whereasshowings therein are for purposes of illustrating embodiments of thepresent disclosure and not for purposes of limiting the same.

DETAILED DESCRIPTION

In the following description, specific details are set forth describingsome embodiments consistent with the present disclosure. It will beapparent, however, to one skilled in the art that some embodiments maybe practiced without some or all of these specific details. The specificembodiments disclosed herein are meant to be illustrative but notlimiting. One skilled in the art may realize other elements that,although not specifically described here, are within the scope and thespirit of this disclosure. In addition, to avoid unnecessary repetition,one or more features shown and described in association with oneembodiment may be incorporated into other embodiments unlessspecifically described otherwise or if the one or more features wouldmake an embodiment non-functional.

FIG. 1 illustrates, in block diagram format, an example embodiment of acomputing system adapted for implementing one or more embodimentsdisclosed herein to optimize runtime environments for applications. Asshown, a computing system 100 may comprise or implement a plurality ofservers, devices, and/or software components that operate to performvarious methodologies in accordance with the described embodiments.Example servers, devices, and/or software components may include, forexample, stand-alone and enterprise-class servers operating a operatingsystem (OS) such as a MICROSOFT® OS, a UNIX® OS, a LINUX® OS, SymbianOS™, iOS, Android, Embedix OS, Binary Run-time Environment for Wireless(BREW) OS, JavaOS, a Wireless Application Protocol (WAP) OS and/or othersuitable OS. It may be appreciated that the servers illustrated in FIG.1 may be deployed in other ways and that the operations performed and/orthe services provided by such servers may be combined, distributed,and/or separated for a given implementation and may be performed by agreater number or fewer number of servers. One or more servers may beoperated and/or maintained by the same or different entities.

Computing system 100 may include, among various devices, servers,databases and other elements, one or more clients 102 that may compriseor employ one or more client devices 104, such as a laptop, a mobilecomputing device, a tablet, a PC, a wearable device, a server decvice,and/or any other computing device having computing and/or communicationscapabilities in accordance with the described embodiments. Clientdevices 104 may include a cellular telephone, smart phone, electronicwearable device (e.g., smart watch, virtual reality headset), and/or thelike. In some examples, client devices 104 may be developer devices fordeveloping applications.

Client devices 104 generally may include one or more programs 106, suchas system programs and application programs to perform various computingand/or communications operations. Example system programs may include,without limitation, an operating system (e.g., MICROSOFT® OS, UNIX® OS,LINUX® OS, Symbian OS™, iOS, Android, Embedix OS, Binary Run-timeEnvironment for Wireless (BREW) OS, JavaOS, a Wireless ApplicationProtocol (WAP) OS, and others), device drivers, programming tools,utility programs, software libraries, application programming interfaces(APIs), and so forth. Example application programs may include, withoutlimitation, developer tools, new applications for development, emulationtools, web browser application, messaging application, contactsapplication, calendar application, electronic document application,database application, media application (e.g., music, video,television), location-based services (LBS) application (e.g., GPS,mapping, directions, positioning systems, geolocation,point-of-interest, locator) that may utilize hardware components such asan antenna, and so forth. One or more of client programs 106 may displayvarious graphical user interfaces (GUIs) to present information toand/or receive information from one or more users of client devices 104.In some embodiments, client programs 106 may include one or moreapplications configured to conduct some or all the functionalitiesand/or processes discussed below.

As shown, client devices 104 may be communicatively coupled via one ormore networks 108 to a network-based system 110. Network-based system110 may be structured, arranged, and/or configured to allow client 102to establish one or more communications sessions between network-basedsystem 110 and various computing devices 104 and/or client programs 106.Accordingly, a communications session between client devices 104 andnetwork-based system 110 may involve the unidirectional and/orbidirectional exchange of information and may occur over one or moretypes of networks 108 depending on the mode of communication. While theembodiment of FIG. 1 illustrates a computing system 100 deployed in aclient-server operating environment, it is to be understood that othersuitable operating environments and/or architectures may be used inaccordance with the described embodiments.

Data communications between client devices 104 and the network-basedsystem 110 may be sent and received over one or more networks 108 suchas the Internet, a WAN, a WWAN, a WLAN, a mobile telephone network, alandline telephone network, personal area network, as well as othersuitable networks. For example, client devices 104 may communicate withnetwork-based system 110 over the Internet or other suitable WAN or LANby sending and or receiving information via interaction with a website,web server, and/or the like. Any of a wide variety of suitablecommunication types between client devices 104 and system 110 may takeplace, as will be readily appreciated. In particular, wireless and wiredcommunications of any suitable form may take place between client device104 and system 110, such as that which often occurs in the case ofnetworked devices and/or servers.

Network-based system 110 may comprise one or more communications servers120 to provide suitable interfaces that enable communication usingvarious modes of communication and/or via one or more networks 108.Communications servers 120 may include a web server 122, an API server124, and/or a messaging server 126 to provide interfaces to one or moreapplication servers 130. Application servers 130 of network-based system110 may be structured, arranged, and/or configured to provide variousonline services to client devices that communicates with network-basedsystem 110. In various embodiments, client devices 104 may communicatewith application servers 130 of network-based system 110 via one or moreof a web interface provided by web server 122, a programmatic interfaceprovided by API server 124, and/or a messaging interface provided bymessaging server 126. It may be appreciated that web server 122, APIserver 124, and messaging server 126 may be structured, arranged, and/orconfigured to communicate with various types of client devices 104,and/or client programs 106 and may interoperate with each other in someimplementations.

Web server 122 may be arranged to communicate with web clients and/orapplications such as a web browser, web browser toolbar, desktop widget,mobile widget, web-based application, web-based interpreter, virtualmachine, mobile applications, and so forth. API server 124 may bearranged to communicate with various client programs 106 comprising animplementation of API for network-based system 110. Messaging server 126may be arranged to communicate with various messaging clients and/orapplications such as e-mail, IM, SMS, MMS, telephone, VoIP, videomessaging, IRC, and so forth, and messaging server 126 may provide amessaging interface to enable access by client 102 to the variousservices and functions provided by application servers 130.

Application servers 130 of network-based system 110 may be a server thatprovides various services to client devices, such as runtime environmentoptimizations for certain applications. Application servers 130 mayinclude multiple servers and/or components. For example, applicationservers 130 may include one or more virtual machines 132, resourcemonitor 134, efficiency analyzer 136, runtime environment generator 138,runtime environment configuration mixer 140, fitness analyzer 142,testing engine 144, and/or runtime environment selector 146. Theseservers and/or components, which may be in addition to other servers,may be structured and arranged to help protect devices against malware.

Application servers 130, in turn, may be coupled to and capable ofaccessing one or more databases 150 including configuration database152, resource log database 154, and/or runtime environment database 156.Databases 150 generally may store and maintain various types ofinformation for use by application servers 130 and may comprise or beimplemented by various types of computer storage devices (e.g., servers,memory) and/or database structures (e.g., relational, object-oriented,hierarchical, dimensional, network) in accordance with the describedembodiments.

FIG. 2 illustrates an example computer system 200 in block diagramformat suitable for implementing on one or more devices of the computingsystem in FIG. 1. In various implementations, a device that includescomputer system 200 may comprise a personal computing device (e.g., asmart or mobile phone, a computing tablet, a personal computer, laptop,wearable device, PDA, etc.) that is capable of communicating with anetwork. A service provider may utilize a network computing device(e.g., a server) capable of communicating with the network. It should beappreciated that each of the devices utilized by users, serviceproviders, and/or the like may be implemented as computer system 200 ina manner as follows.

Additionally, as more and more devices become communication capable,such as new smart devices using wireless communication to report, track,message, relay information and so forth, these devices may be part ofcomputer system 200. For example, windows, walls, and other objects maydouble as touchscreen devices for users to interact with. Such devicesmay be incorporated with the systems discussed herein.

Computer system 200 may include a bus 202 or other communicationmechanisms for communicating information data, signals, and informationbetween various components of computer system 200. Components include aninput/output (I/O) component 204 that processes a user action, such asselecting keys from a keypad/keyboard, selecting one or more buttons,links, actuatable elements, etc., and sends a corresponding signal tobus 202. I/O component 204 may also include an output component, such asa display 211 and a cursor control 213 (such as a keyboard, keypad,mouse, touchscreen, etc.). In some examples, I/O component 204 mayinclude an image sensor for capturing images and/or video, such as acomplementary metal-oxide semiconductor (CMOS) image sensor, and/or thelike. An audio input/output component 205 may also be included to allowa user to use voice for inputting information by converting audiosignals. Audio I/O component 205 may allow the user to hear audio. Atransceiver or network interface 206 transmits and receives signalsbetween computer system 200 and other devices, such as another userdevice, a merchant server, an email server, application serviceprovider, web server, a payment provider server, and/or other serversvia a network. In various embodiments, such as for many cellulartelephone and other mobile device embodiments, this transmission may bewireless, although other transmission mediums and methods may also besuitable. A processor 212, which may be a micro-controller, digitalsignal processor (DSP), or other processing component, processes thesevarious signals, such as for display on computer system 200 ortransmission to other devices over a network 260 via a communicationlink 218. Again, communication link 218 may be a wireless communicationin some embodiments. Processor 212 may also control transmission ofinformation, such as cookies, IP addresses, images, and/or the like toother devices.

Components of computer system 200 also include a system memory component214 (e.g., RAM), a static storage component 216 (e.g., ROM), and/or adisk drive 217. Computer system 200 performs specific operations byprocessor 212 and other components by executing one or more sequences ofinstructions contained in system memory component 214. Logic may beencoded in a computer-readable medium, which may refer to any mediumthat participates in providing instructions to processor 212 forexecution. Such a medium may take many forms, including but not limitedto, non-volatile media, volatile media, and/or transmission media. Invarious implementations, non-volatile media includes optical or magneticdisks, volatile media includes dynamic memory such as system memorycomponent 214, and transmission media includes coaxial cables, copperwire, and fiber optics, including wires that comprise bus 202. In oneembodiment, the logic is encoded in a non-transitory machine-readablemedium. In one example, transmission media may take the form of acousticor light waves, such as those generated during radio wave, optical, andinfrared data communications.

Some common forms of computer readable media include, for example,floppy disk, flexible disk, hard disk, magnetic tape, any other magneticmedium, CD-ROM, any other optical medium, punch cards, paper tape, anyother physical medium with patterns of holes, RAM, PROM, EPROM,FLASH-EPROM, any other memory chip or cartridge, or any other mediumfrom which a computer is adapted to read.

In various embodiments of the present disclosure, execution ofinstruction sequences to practice the present disclosure may beperformed by computer system 200. In various other embodiments of thepresent disclosure, a plurality of computer systems 200 coupled bycommunication link 218 to the network (e.g., such as a LAN, WLAN, PTSN,and/or various other wired or wireless networks, includingtelecommunications, mobile, and cellular phone networks) may performinstruction sequences to practice the present disclosure in coordinationwith one another. Modules described herein may be embodied in one ormore computer readable media or be in communication with one or moreprocessors to execute or process the techniques and algorithms describedherein.

A computer system may transmit and receive messages, data, informationand instructions, including one or more programs (i.e., applicationcode) through a communication link and a communication interface.Received program code may be executed by a processor as received and/orstored in a disk drive component or some other non-volatile storagecomponent for execution.

Where applicable, various embodiments provided by the present disclosuremay be implemented using hardware, software, or combinations of hardwareand software. Also, where applicable, the various hardware componentsand/or software components set forth herein may be combined intocomposite components comprising software, hardware, and/or both withoutdeparting from the spirit of the present disclosure. Where applicable,the various hardware components and/or software components set forthherein may be separated into sub-components comprising software,hardware, or both without departing from the scope of the presentdisclosure. In addition, where applicable, it is contemplated thatsoftware components may be implemented as hardware components andvice-versa.

Software, in accordance with the present disclosure, such as programcode and/or data, may be stored on one or more computer-readable media.It is also contemplated that software identified herein may beimplemented using one or more computers and/or computer systems,networked and/or otherwise. Such software may be stored and/or used atone or more locations along or throughout the system, at client 102,network-based system 110, or both. Where applicable, the ordering ofvarious steps described herein may be changed, combined into compositesteps, and/or separated into sub-steps to provide features describedherein.

The foregoing networks, systems, devices, and numerous variationsthereof may be used to implement one or more services, such as theservices discussed above and in more detail below.

FIG. 3 illustrates a flow diagram for an example runtime environmentoptimization process 300. Process 300 may be implemented on a systemsuch as system 100 of FIG. 1 according to some embodiments. According tosome embodiments, process 300 may include one or more of operations301-309, which may be implemented, at least in part, in the form ofexecutable code stored on a non-transitory, tangible, machine readablemedia that, when run on one or more processors, may cause a system toperform one or more of the operations 301-309.

At operation 301, the system may receive a request to determine anoptimal server and/or runtime environment for one or more applications.The system may receive the applications for use in the runtimeenvironment and/or one or more restrictions or parameters, such as theoperating system used, hardware constraints (e.g. memory space, databasespace, and/or the like), and/or other restrictions. For example, beforereleasing a new application to the public, a user may want to optimizethe runtime environment for use, and the user may use the system toprovide configurations for an optimized runtime environment for one ormore applications.

Process 300 may include operation 302. At operation 302, the system mayone or more initial runtime environments for testing with theapplication received at operation 301 and using to iteratively determinean optimized runtime environment. For example, the system may have oneor more plurality of default settings for runtime environments to testapplications on. In some examples, the system may have several runtimeenvironment settings that the system may have determined for prioroptimizations for other applications and saved in a database. In someexamples, the system may randomly select settings for the runtimeenvironment. In yet another example, the system may take a runtimeenvironment with default settings and randomly add changes to thesettings. Furthermore, the system may use a combination of the methodsdescribed above to generate a plurality of runtime environments to testwith the applications received at operation 301. In some examples, eachof the plurality of runtime environments may have unique settings fromeach other. Furthermore, the system may conduct operation 302 forseveral different engines that could be used for the runtimeenvironment. In this manner, the system may optimize the runtimeenvironments for multiple engines to help determine which engines aresuitable for the one or more applications and/or what differentoptimizations could be made based on the engine. Furthermore, this mayhelp the system determine which engine is optimal for the one or moreapplications.

In some examples, the runtime environments may be run in a virtualmachine emulating the environment. Furthermore, there may be somelimitations on the resources available for the runtime environments dueto the device running the virtual machine, the number of virtualmachines, and/or the like. In some embodiments, the runtime environmentmay be run in a physical machine.

Process 300 may include operation 303. At operation 303, the system mayset up one or more of the runtime environment and run the applicationsin the runtime environments with test data. For example, the system maysetup a virtual machine with a runtime environment configurationselected from one of the plurality of runtime environments determined atoperation 302. The configurations may include, but are not limited to,hardware specifications (e.g. processor speeds, number of processors,number of processor cores, memory (virtual and physical) size, diskspace, disk speed, fs cluster size, and/or the like), I/O configurations(e.g. I/O cashe size, I/O max load, speed, and/or the like), networkcapabilities (e.g. number of ports, TCP/IP buffer sizes, flow controlconfigurations, and/or the like), heap size, database configurations(e.g. data persistence settings, query optimization levels, buffpage,and/or the like), and/or other runtime environment settings.

With the runtime environment(s) up and running and the applicationsinstalled and running in the runtime environments, the system mayattempt to emulate one or more loads using the test data for theapplications running in the runtime environments. For example, the testdata may be interactions specific for the applications, such as APIcalls, service requests, queries, use of libraries, and/or the like. Thetest data may be provided for the system to test with the applications.In some examples, the test data may be prerecorded in a database foruse. In some examples, the test data may be splits from real time ornear real time operations and queries for another application or runtimeenvironment. As an example, there may be an older version of anapplication that is running live and in public and the application beingtested through process 300 may be a newer version. As such, the systemcould fork and/or duplicate requests that are being conducted for theolder application for use as test data for the new application in theruntime environment set up by process 300. In this manner the system cantest the application and runtime environments using realistic test data.

Process 300 may include operation 304. At operation 304, the system maymeasure one or more attributes of the runtime environment and determineone or more fitness values for each runtime environment. For example,the system may monitor, for optimization, resources that include, butare not limited to, memory allocation (virtual and/or physica), pagedpool, non-paged pool, data storage used, count of read bytes, count ofwrite bytes, latency in read, latency in write, number of networkconnections, count of network read bytes, count of network write bytes,network latency for read and/or write, CPU usage (mininum and/ormaximum), CPU usage time, CPU time in user mode, CPU time in kernelmode, user response times, size of stack, size of heap, minimum numberof processes, maximum number of processes, minimum number of filedescriptors, maximum number of file descriptors, CPU affinityconfiguration, lengths of time associated with resources usages, and/orthe like. The system may also measure values associated with theapplication, such as response times, error rates, and/or the like.

The system may determine averages for the characteristics, and/or otherstatistical representations, such as frequency of certaincharacteristics. For example, frequency of CPU usage below a certainthreshold and/or above a certain threshold.

Furthermore, the system may determine one or more fitness values foreach of the different runtime environments based on the individualstatistical values measured while running the applications in theruntime environment. In some examples, the aggregated statistical valuesin combination may be used as the fitness values. In some examples, thesystem may determine one or more representative values associated withthe fitness of the runtime environment. The fitness may be for resourceefficiency while still capable of optimal performance of theapplications. In some examples, the system may be configured to balanceefficiency with performance. An example of high efficiency for a CPUresource would be when the CPU is used at 100% all the time. However,this may be an example of high efficiency, but poor performance due theCPU being too slow. In contrast, a resource may allow for optimalperformance but may be highly inefficient. For example, if a maximum of1mb of memory is used by a system that has 10 gb of memory, this wouldclearly be highly inefficient. These examples can be applied to manyother resources, such as buffer size, heap size, data rate, bandwidth,and/or the like, all of which the process 300 may be optimizing for.

In some examples, a user may provide some optimizations parameters thatthe system may use as constraints as part of the optimization andfitness determination. For example, a user may request that one or moreresources, such as the CPU, not be used above 90% for over a certaintime period. In some examples, the optimization parameters may be a goalassociated with an application, such as to optimize the system for theshortest response times for the applications run in the runtimeenvironment. In some examples, the optimization may be to ensure aresource is used above a threshold value, or that speeds are not a belowa certain level, or not below a certain level for a % of time and/or fora length of time. The fitness score determined by the system may bebased at least in part on the performance of the runtime environmentsagainst the user provided constraints. In some examples, the system mayattempt to optimize the system without constraints.

In some examples, the system may optimize performance and maximizingefficiency at the same time. The system may use averages and/orcomparisons of statistical values of the different runtime environmentsmeasured to determine whether performance has been maximized and/orwhether efficiency has been maximized. For example, the system maycompare memory usage and determine that memory was not used above acertain amount, that additional CPU speed did not change response times,and/or the like. The system may maintain a record of maximum and minimumvalues tested from the different runtime environments as examples ofoptimal performance and/or optimal efficiency.

In some examples, the system may determine efficiency values,performance values, and/or a combination value runtime environmentsbased on the statistics gathered during the run in operation 303. Insome examples the values may be based on deviations from the average ofall the runtime environments. In some examples, the values may be basedon deviations from a maximum or a minimum. In some examples, the valuesmay be based on deviations from user constraints. In some examples, thesystem may aggregate the values (some of the values may be weighteddifferently) to determine fitness scores for each of the runtimeenvironments for ranking.

Process 300 may include operation 305. At operation 305, the system mayselect a subset of runtime environments run at operation 303. In someexamples, the system may select a predetermined number or percentage ofruntime environments with the best fitness scores determined atoperation 304. In some examples, one or more runtime environments may beselected at random to add diversity. In some examples, one or moreruntime environments may be selected based on extreme values, such asthe fastest response time, least CPU usage, most CPU usage, and/or thelike. In some examples, the system may use a combination of methods toselect the subset of runtime environments.

Process 300 may include operation 306. At operation 306, the system mayimplement one or more genetic or evolution algorithm techniques on thesubset of runtime environments selected at operation 305 to create a newset of runtime environments. For example, the system may mix two or moreruntime environment configurations and/or apply one or more mutations onone or more runtime environments.

In some examples, the system may apply a cross over technique on two ormore runtime environments. For example, the system may have two runtimeenvironments trade half of the configurations. The crossover points maybe predetermined, selected at random, or selected using one or morealgorithms. In some examples the cross over point may determine how muchof a runtime environment changes. In some examples, the system may setthe change to a predetermined percentage of change. For example, having20% of the configurations on a runtime environment change, and 80% keptthe same. In some examples, the system may apply the partially matchedcrossover technique, where two crossover points are selected at random.

Other crossover techniques may be used in combination or instead. Forexample, the system may use cycle cross over where every Nthconfiguration is replaced with a configuration from another runtimeenvironment in the subset determine at operation 305. N may bepredetermined. For example, if N was 2, every other configuration wouldbe switched.

In some examples, the system may use one or more other cross overoperations, such as order-based cross over, position-based crossover,voting recombination crossover, alternating-position cross over,sequential constructive cross over, and/or the like. In this manner, thesystem may attempt to create a better or more optimized runtimeenvironment by merging the runtime environments that had the betterfitness scores.

Furthermore, the system may mutate one or more of the configurations forthe runtime environments, sometimes before a crossover and/or sometimesafter. The system may implement one or more methods of mutations. Forexample, the system may randomly replace a configuration with a upper orlower boundary. In some examples, the system may increase or decrease arandom configuration value. In some examples, the system may apply arandom value for one or more random configuration settings. In thismanner, additional diversity of configuration is maintained. In someexamples, the amount of mutation may be predetermined and/or provided bya user. For example, maybe only a subset of the subset of runtimeenvironments may be mutated, and the mutations may be limited to apredetermine percentage of configurations.

Operation 306 may result in a plurality of additional runtimeenvironments derived from the subset determined at operation 305. Theadditional runtime environments may be a combination and/or mutationsfrom the subsets.

In some examples, process 300 may include operation 307. At operation307, the system may determine whether the genetic algorithm and/orevolutionary process for determining the optimal configuration of theruntime environments for the applications in operation 301 has beenperformed a sufficient number of times. In some examples, there may be apredetermine number of genetic algorithm iterations/generations that thesystem goes through. In some examples, genetic algorithm iterations maybe characterized by a set of operations that can be repeated in afeedback loop as part of an evolution using a genetic algorithm, such asone or more of operations 303-306. In some examples, the user maydetermine the number of genetic algorithm iterations. In some examples,the numbers of genetic algorithm iterations may be hard coded. In someexamples, the system may determine the number of genetic algorithmiterations to perform based on the number of runtime environments wereused to initiate the genetic algorithm iterations.

In some examples, the system may determine whether enough geneticalgorithm iterations have been conducted based on how much change to thefitness scores occurs between genetic algorithm iterations. For example,if the delta between fitness scores is zero (or other threshold deltavalue) for a genetic algorithm iteration and/or a predetermined numberof genetic algorithm iterations, the system may determine that thegenetic algorithm iterations should end. In some examples, the thresholddelta may depend on an average change in delta from previous geneticalgorithm iterations. In this manner, the system does not unnecessarilycontinue testing and creating new runtime environments to obtain theoptimal solution, when the optimal solution may have already beenachieved.

If the system determine that additional genetic algorithm iterationsshould be performed, the system may continue to operation 303 using thenew runtime environments as input for testing the applications atoperation 303.

If the system determines that additional genetic algorithm iterationsare not required, the system may continue to operation 308, wherein thefitness scores of the newly determined runtime environments at operation305. In some examples, the system may obtain the fitness scores in asimilar manner as operations 303 and 304.

Process 300 may include operation 309, wherein the system selects one ofthe runtime environments with the best fitness score as the optimalruntime environment.

The foregoing disclosure is not intended to limit the present disclosureto the precise forms or particular fields of use disclosed. As such, itis contemplated that various alternate embodiments and/or modificationsto the present disclosure, whether explicitly described or impliedherein, are possible in light of the disclosure.

Having thus described embodiments of the present disclosure, persons ofordinary skill in the art will recognize that changes may be made inform and detail without departing from the scope of the presentdisclosure. Thus, the present disclosure is limited only by the claims.

What is claimed is:
 1. A system, comprising: a non-transitory memorystoring instructions; and one or more hardware processors coupled to thenon-transitory memory and configured to execute the instructions fromthe non-transitory memory to cause the system to perform operationscomprising: initiating and performing a plurality of executions of agenetic algorithm iteration with a first input for a first execution ofthe plurality of executions and using a result from each execution ofthe plurality of executions of the genetic algorithm iteration as theinput of each subsequent execution of the plurality of executions of thegenetic algorithm iteration, the genetic algorithm iteration ofoperations using a plurality of test runtime environments as input, eachof the plurality of test runtime environments having configurations, thegenetic algorithm iteration of operations comprising: running one ormore applications with test data for each of the plurality of testruntime environments; determining a fitness value for each of theplurality of test runtime environments; selecting a subset of theplurality of runtime environments based on the fitness value for each ofthe plurality of test runtime environments; and outputting a pluralityof second test runtime environments, each of the second test runtimeenvironments having second configurations that are a mixture of theconfigurations from two or more runtime environments selected from thesubset of the plurality of runtime environments; and selecting a finalconfiguration from a final output of the plurality of executions of thegenetic algorithm iteration of operations.
 2. The system of claim 1,wherein the final configuration is selected based on a fitness valuedetermined from running the one or more applications with the test datafor each of a plurality of test runtime environments in the finaloutput.
 3. The system of claim 1, wherein a number of the plurality ofexecutions of the genetic algorithm iteration of operations is based ona difference in fitness scores between two genetic algorithm iterations.4. The system of claim 1, wherein the fitness value is based at least inpart on one or more attributes selected from a group consisting of CPUload, memory load, and network load.
 5. The system of claim 1, whereinthe genetic algorithm iteration of operations further comprisesintroducing a mutation to the configurations of one or more runtimeenvironments in the subset of the plurality of runtime environments orone or more runtime environments of the plurality of second test runtimeenvironments.
 6. The system of claim 1, wherein the mixture ofconfigurations is created using a multipoint crossover operation.
 7. Thesystem of claim 6, wherein the crossover operation has at least onebiased crossover point based on one more previously determined fitnessvalues determined for a plurality of runtime environments.
 8. The systemof claim 1, wherein the operations further comprises determining aminimum resources for obtaining peak performance running the one or moreapplications.
 9. A computer implemented method, comprising: initiatingand performing a plurality of executions of a genetic algorithmiteration of operations with a first input for a first execution of theplurality of executions and using a result from each execution of theplurality of executions of the genetic algorithm iteration as the inputof each subsequent execution of the plurality of executions of thegenetic algorithm iteration, the genetic algorithm iteration ofoperations using a plurality of test runtime environments as input, eachof the plurality of test runtime environments having configurations, thegenetic algorithm iteration of operations comprising: running one ormore applications with test data for each of the plurality of testruntime environments; determining a fitness value for each of theplurality of test runtime environments; selecting a subset of theplurality of runtime environments based on the fitness value for each ofthe plurality of test runtime environments; and outputting a pluralityof second test runtime environments, each of the second test runtimeenvironments having second configurations that are a mixture of theconfigurations from two or more runtime environments selected from thesubset of the plurality of runtime environments; and selecting a finalconfiguration from a final output of the plurality of executions of thegenetic algorithm iteration of operations.
 10. The method of claim 9,further comprising receiving at least one minimum requirement from auser.
 11. The method of claim 10, wherein the minimum requirement is athreshold memory usage.
 12. The method of claim 9, wherein the mixtureof configurations is created using a multipoint crossover operation. 13.The method of claim 12, wherein a crossover point used in the multipointcrossover operation is based at least in part on a change in a change inruntime environment performance from a previously genetic algorithmiteration.
 14. The method of claim 9, further comprising determining amaximum usage of a resource from the running of the one or moreapplications with the test data for each of the plurality of runtimeenvironments.
 15. The method of claim 14, further comprising determininga configuration threshold based on the determining of the maximum usage.16. The method of claim 15, wherein the maximum usage determined is usedas a configuration condition for selecting the final configuration. 17.A non-transitory computer-readable medium having stored thereoninstructions executable by a computer to cause the computer to performoperations comprising: initiating and performing a plurality ofexecutions of a genetic algorithm iteration of operations with a firstinput for a first execution of the plurality of executions and using aresult from each execution of the plurality of executions of the geneticalgorithm iteration as the input of each subsequent execution of theplurality of executions of the genetic algorithm iteration, the geneticalgorithm iteration of operations using a plurality of test runtimeenvironments as input, each of the plurality of test runtimeenvironments having configurations, the genetic algorithm iteration ofoperations comprising: running one or more applications with test datafor each of the plurality of test runtime environments; determining afitness value for each of the plurality of test runtime environments;selecting a subset of the plurality of runtime environments based on thefitness value for each of the plurality of test runtime environments;and outputting a plurality of second test runtime environments, each ofthe second test runtime environments having second configurations thatare a mixture of the configurations from two or more runtimeenvironments selected from the subset of the plurality of runtimeenvironments; and selecting a final configuration from a final output ofthe plurality of executions of the genetic algorithm iteration ofoperations.
 18. The non-transitory computer-readable medium of claim 17,wherein at least one of the runtime environment in the subset of theplurality of runtime environments is selected at least based in part forhaving a fastest response time.
 19. The non-transitory computer-readablemedium of claim 18, wherein the fastest response time is in relation toan operation performed by the one or more applications.
 20. Thenon-transitory computer-readable medium of claim 18, wherein the fitnessvalue is dependent at least in part on a user provided criterion.